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Freshwater mussels have played an important role in the history of human culture and also in ecosystem functioning. But during the past several decades, the abundance and diversity of mussel species has declined all over the world. To address the urgent need to maintain and restore populations of endangered freshwater mussels, quantitative population dynamics modeling is needed to evaluate population status and guide the management of endangered freshwater mussels. One endangered mussel species, the oyster mussel (Epioblasma capsaeformis), was selected to study its population dynamics for my research. The analysis was based on two datasets, length frequency data from annual surveys conducted at three sites in Clinch River: Wallen Bend (Clinch River Mile 192) from 2004-2010, Frost Ford (CRM 182) from 2005 to 2010 and Swan Island (CRM 172) from 2005 to 2010, and age-length data based on shell thin-sections. Three hypothetical scenarios were assumed in model estimations: (1) constant natural mortality; (2) one constant natural mortality rate for young mussels and another one for adult mussels; (3) age-specific natural mortality. A Bayesian approach was used to analyze the age-structured models and a Bayesian model averaging approach was applied to average the results by weighting each model using the deviance information criterion (DIC). A risk assessment was conducted to evaluate alternative restoration strategies for E. capsaeformis. The results indicated that releasing adult mussels was the quickest way to increase mussel population size and increasing survival and fertility of young mussels was a suitable way to restore mussel populations in the long term. The population of E. capsaeformis at Frost Ford had a lower risk of decline compared with the populations at Wallen Bend and Swan Island. Passive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively. To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex--related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small. / Master of Science

We present distances and kinematics and look at the possible populations for the blue plume (BP) stars in the Canis Major Overdensity (CMa). We conducted a medium resolution spectral survey on the BP stars (N=303) in CMa (centered at l = 238° ; b = -8°) using the data from AAOmega Spectrograph. We used a modified version of the Statistics-sensitive Non-linear Iterative Peak-clipping (SNIP) algorithm to normalize our fluxed absorption spectra. After determining the radial velocities from measurements of strong absorption features for the stars we use a Bayesian analysis of spectral feature strengths and photometric colors to determine Teff, Logg and [Fe/H]. Our procedure makes use of grid for model synthetic spectra computed using SPECTRUM with Atlas9 model atmospheres and Kurucz model colors. We determine the absolute magnitude using the stellar parameters and BaSTI isochrones and compute distances and ages for the BP stars. Our analysis of the BP stars indicates Teff ranging from 6500K to 8000K, metallicity ranging from 0.0 to -1.0 with an average of -0.5. We found for this temperature range that the surface gravity of the stars could not be well constrained. From the spatial and kinematics results we found that most of the stars are thick disk stars with a small mixture of thin disk stars. The stars are most likely a mixture of thick disk blue stragglers and normal A-type stars preferentially seen to greater depths due to the low dust extinction in this location of the Galaxy.

Thesis (MSc)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Techniques for estimating a signal in the presence of noise which contains outliers are currently not well developed. In this thesis, we consider a constant signal superimposed by a family of noise distributions structured as a tunable mixture f(x) = α g(x) + (1 − α) h(x) between finitesupport components of “well-behaved” noise with small variance g(x) and of “impulsive” noise h(x) with a large amplitude and strongly asymmetric character. When α ≈ 1, h(x) can for example model a cosmic ray striking an experimental detector. In the first part of our work, a method for obtaining the expected values of the positive and negative pulses in the first resolution level of a LULU Discrete Pulse Transform (DPT) is established. Subsequent analysis of sequences smoothed by the operators L1U1 or U1L1 of LULU-theory shows that a robust estimator for the location parameter for g is achieved in the sense that the contribution by h to the expected average of the smoothed sequences is suppressed to order (1 − α)2 or higher. In cases where the specific shape of h can be difficult to guess due to the assumed lack of data, it is thus also shown to be of lesser importance. Furthermore, upon smoothing a sequence with L1U1 or U1L1, estimators for the scale parameters of the model distribution become easily available. In the second part of our work, the same problem and data is approached from a Bayesian inference perspective. The Bayesian estimators are found to be optimal in the sense that they make full use of available information in the data. Heuristic comparison shows, however, that Bayes estimators do not always outperform the LULU estimators. Although the Bayesian perspective provides much insight into the logical connections inherent in the problem, its estimators can be difficult to obtain in analytic form and are slow to compute numerically. Suboptimal LULU-estimators are shown to be reasonable practical compromises in practical problems. / AFRIKAANSE OPSOMMING: Tegnieke om ’n sein af te skat in die teenwoordigheid van geraas wat uitskieters bevat is tans nie goed ontwikkel nie. In hierdie tesis aanskou ons ’n konstante sein gesuperponeer met ’n familie van geraasverdelings wat as verstelbare mengsel f(x) = α g(x) + (1 − α) h(x) tussen eindige-uitkomsruimte geraaskomponente g(x) wat “goeie gedrag” en klein variansie toon, plus “impulsiewe” geraas h(x) met groot amplitude en sterk asimmetriese karakter. Wanneer α ≈ 1 kan h(x) byvoorbeeld ’n kosmiese straal wat ’n eksperimentele apparaat tref modelleer. In die eerste gedeelte van ons werk word ’n metode om die verwagtingswaardes van die positiewe en negatiewe pulse in die eerste resolusievlak van ’n LULU Diskrete Pulse Transform (DPT) vasgestel. Die analise van rye verkry deur die inwerking van die gladstrykers L1U1 en U1L1 van die LULU-teorie toon dat hul verwagte gemiddelde waardes as afskatters van die liggingsparameter van g kan dien wat robuus is in die sin dat die bydrae van h tot die gemiddeld van orde grootte (1 − α)2 of hoër is. Die spesifieke vorm van h word dan ook onbelangrik. Daar word verder gewys dat afskatters vir die relevante skaalparameters van die model maklik verkry kan word na gladstryking met die operatore L1U1 of U1L1. In die tweede gedeelte van ons werk word dieselfde probleem en data vanuit ’n Bayesiese inferensie perspektief benader. Die Bayesiese afskatters word as optimaal bevind in die sin dat hulle vol gebruikmaak van die beskikbare inligting in die data. Heuristiese vergelyking wys egter dat Bayesiese afskatters nie altyd beter vaar as die LULU afskatters nie. Alhoewel die Bayesiese sienswyse baie insig in die logiese verbindings van die probleem gee, kan die afskatters moeilik wees om analities af te lei en stadig om numeries te bereken. Suboptimale LULU-beramers word voorgestel as redelike praktiese kompromieë in praktiese probleme.

This thesis investigates the use of Bayesian analysis upon an opponent's behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent's actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes' theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes' rule yields a notable improvement in the performance of an agent once an opponent's style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold'em, where a betting round-based approach proves useful in determining and counteracting an opponent's play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent 'style'.

Groundwater is a major source of water supply, and aquifers form major storage reservoirs as well as water conveyance systems, worldwide. The viability of groundwater as a source of water to the world's population is threatened by overexploitation and contamination. The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, water flow and contaminant transport. As the collection of subsurface characterization and monitoring data is costly, it is imperative that the design of corresponding data collection schemes is cost-effective. A major benefit of new data is its potential to help improve one's understanding of the system, in large part through a reduction in model predictive uncertainty and corresponding risk of failure. Traditionally, value-of-information or data-worth analyses have relied on a single conceptual-mathematical model of site hydrology with prescribed parameters. Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty. This has led to a recent emphasis on conducting hydrologic analyses and rendering corresponding predictions by means of multiple models. We develop a theoretical framework of data worth analysis considering model uncertainty, parameter uncertainty and potential sample value uncertainty. The framework entails Bayesian Model Averaging (BMA) with emphasis on its Maximum Likelihood version (MLBMA). An efficient stochastic optimization method, called Differential Evolution Method (DEM), is explored to aid in the design of optimal sampling schemes aiming at maximizing data worth. A synthetic case entailing generated log hydraulic conductivity random fields is used to illustrate the procedure. The proposed data worth analysis framework is applied to field pneumatic permeability data collected from unsaturated fractured tuff at the Apache Leap Research Site (ALRS) near Superior, Arizona.

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Nesta tese propomos dois modelos espaciais de captura-recaptura para estimação da abundância populacional em população aberta. Os modelos estatísticos propostos ajustam-se a dados obtidos via amostragem de captura-recaptura com marcação individual realizada em diferentes locais dentro do habitat, levando em consideração as taxas de nascimentos e mortes durante o período de estudo e as localizações geográficas das capturas. No primeiro modelo, propomos uma modelagem hierárquica para os tamanhos populacionais locais a fim de obter a distribuição preditiva da abundância populacional para regiões não visitadas pela amostragem. Nesta etapa, uma estrutura para dados zero-inflacionados foi adotada para acomodar situações quando realizam-se amostragens em locais sem a presença da espécie. O segundo modelo proposto leva em consideração o deslocamento dos animais entre os diferentes locais de amostragem, generalizando o primeiro modelo no qual consideramos a permanência dos animais em um mesmo local. Neste caso, tornou-se possível estimar o tamanho da área de vida (movimentação) da espécie além de predizer locais com maiores abundâncias de animais. Em ambos modelos, propomos uma abordagem bayesiana para o processo inferencial e derivamos algoritmos de simples implementação computacional, a partir do uso de técnicas de dados aumentados. As propriedades frequentistas dos estimadores bayesianos foram avaliadas por meio de estudos de simulação e, por fim, estas propostas de modelagem foram aplicadas a três conjuntos de dados reais de aracnídeos. / In this thesis we propose two spatial capture-recapture models for estimation of population abundance in the open population. The proposed statistical models conform to data obtained through individual tag capture-recapture sampling performed in different areas within the habitat, taking into account the rates of births and deaths during the study period and the geographical locations of the catches. In the first model, we propose a hierarchical modeling for local population sizes in order to obtain the predictive distribution of population abundance for regions not visited by sampling. In this step, a structure for zero-inflated data was adopted to accommodate situations when sampling is performed in areas without the presence of the species. The second proposed model takes into account the movement of the animals among the different sampling areas, generalizing the first model in which we consider the permanence of the animals in the same area. In this case, it became possible to estimate the size of the area of movement of the species and to predict areas with higher abundances of animals. In both models, we propose a Bayesian approach to the inferential process and derive algorithms from simple computational implementation, from the use of augmented data techniques. The frequentist properties of the Bayesian estimators were evaluated by simulation studies and, finally, these modeling proposals were applied to three real data sets of arachnids.

Remembering to complete some future intention (i.e., prospective remembering) is a frequent requirement of everyday activities. Prospective memory failures (e.g., forgetting to take one’s medication) can have devastating consequences. Cognitive psychologists have sought to understand how individuals can successfully fulfill their prospective memory intentions. Unfortunately, it has been difficult to find evidence for specific cognitive mechanisms that could feasibly account for prospective memory behaviors. In part, this is because many theories of prospective memory stipulate that prospective remembering is accomplished through discrete/covert mental processes. In the current set of experiments, eye-tracking technology was used to test these various mechanistic explanations. Using an eye-tracking computer to measure pupillary responses to prospective memory task characteristics allowed for the observation of changes in discrete mental activity during the course of a prospective memory task scenario. Across two experiments, I observed elevated pupil dilation when participants were given additional prospective memory demands. Furthermore, when participants correctly recognized the presentation of a prospective memory target, it appeared that their pupil dilation increased dramatically, and elevated dilation persisted for several trials. This pattern of pupil dilation is consistent with an account of prospective remembering that suggests individuals sometimes engage in actively monitoring for an opportunity to<br>11<br>complete their prospective memory intention, and that at other times, individuals will reduce or discontinue monitoring activity until some cue brings the prospective memory intention back into mind. Consistent with such an account, individual differences in working memory were positively associated with pupil size only when the prospective memory task afforded monitoring. This was in line with recent research implicating the working memory system in facilitating active monitoring during certain prospective memory contexts. Finally, the current set of experiments demonstrated the utility of pupillometric methods for measuring active monitoring in a prospective memory scenario.